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A Neural Network Architecture Using Separable Neural Networks for the Identification of “Pneumonia” in Digital Chest Radiographs

A Neural Network Architecture Using Separable Neural Networks for the Identification of “Pneumonia” in Digital Chest Radiographs
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Author(s): N. Sarada (Koneru Lakshmaiah Education Foundation, India) and K. Thirupathi Rao (Koneru Lakshmaiah Education Foundation, India)
Copyright: 2021
Volume: 17
Issue: 1
Pages: 12
Source title: International Journal of e-Collaboration (IJeC)
Editor(s)-in-Chief: Jingyuan Zhao (University of Toronto, Canada)
DOI: 10.4018/IJeC.2021010106

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Abstract

In recent years, convolutional neural networks had a wide impact in the fields of medical image processing. Image semantic segmentation and image classification have been the main challenges in this field. These two techniques have been seeing a lot of improvement in medical surgeries which are being carried out by robots and autonomous machines. This work will be working on a convolutional model to detect pneumonia in a given chest x-ray scan. In addition to the convolution model, the proposed model consists of deep separable convolution kernels which replace few convolutional layers; one main advantage is these take in a smaller number of parameters and filters. The described model will be more efficient, robust, and fine-tuned than previous models developed using convolutional neural networks. The authors also benchmarked the present model with the CheXnet model, which almost predicts over 16 abnormalities in the given chest-x-rays.

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